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AbstractFull Text

In the European Union there is a strong increase in labelling systems for branding agricultural products and of course heavy competition in the food market. This has led to some demand-driven changes in agricultural production in general and in the cattle and dairy business in particular. About a...

Author(s)
Zottl, K.; Bucek, P.; Kyntäjä, J.; Westhuizen, J. van der; Onken, F.; Lecomte, Ch.; Kucera, J.; Haase, K.; Lavon, Y.
Publisher
International Committee for Animal Recording (ICAR), Rome, Italy
Citation
ICAR Technical Series, 2017, No.21, pp 221-227
AbstractFull Text

Author(s)
Baumgartner, C.; Behr, B.; Hachenberg, S.
Publisher
International Committee for Animal Recording (ICAR), Rome, Italy
Citation
ICAR Technical Series, 2018, No.23, pp 35
AbstractFull Text

The ICAR Recording Guidelines include provision for on-line milk analysis. However the guidelines are inconsistent with some of the practical realities of on-line milk analysis. Consequently, no milk analyser has been able to achieve ICAR approval to date. In the case of somatic cell count (SCC),...

Author(s)
Orchard, R. G.; Anderson, G. P. S.; Winkelman, A. M.
Publisher
International Committee for Animal Recording (ICAR), Rome, Italy
Citation
ICAR Technical Series, 2018, No.23, pp 37-43
AbstractFull Text

Author(s)
Francis, T.; Manglani, C.; McCarthy, S.; Nguyen, A.; Gorst, D.
Publisher
International Committee for Animal Recording (ICAR), Rome, Italy
Citation
ICAR Technical Series, 2018, No.23, pp 97-98
AbstractFull Text

This survey looks at the plausibility checks used at data capture of a few key data sets in milk recording organisations around the world. The aim of these checks is to keep the data integral, with no internal discrepancies. We found that the most important plausibility checks are almost universal, ...

Author(s)
Kyntäjä, J.; Alday, S.; Barras, E.; Bhagat, R. L.; Bucek, P.; Craven, T.; Feeney, L.; Galvanovska, E.; Haase, K.; High, J.; Jóhannesson, G.; Kuwan, K.; Larsson, N. L.; Lauritsen, U.; Lavon, Y.; Mikalsen, V.; Pecinar, M.; Radzio, D.; Rapaioli, F.
Publisher
International Committee for Animal Recording (ICAR), Rome, Italy
Citation
ICAR Technical Series, 2018, No.23, pp 179-183
AbstractFull Text

On-line somatic cell count (SCC) analysers automatically test for SCC during milking for individual cows, providing farmers with more measurements than conventional herd testing. The purpose of this study was to determine whether more frequent milk sampling increases detection of elevated SCC due...

Author(s)
Zhang, I. L.; Williamson, J. H.; Lacy-Hulbert, S. J.
Publisher
International Committee for Animal Recording (ICAR), Rome, Italy
Citation
ICAR Technical Series, 2018, No.23, pp 235-244
AbstractFull Text

Emerging animal health related issues and improved animal health management require sufficient comparable data. In the EADGENE Health Data Comparison Project a pilot study indicated that, both across Europe and between species, animal health data collection is fragmented and lacks harmonisation....

Author(s)
Neeteson, A.; Aumüller, R.; Bleriot, G.; Hoste, S.; Jones, G.; Jones, H.; Malafosse, A.; Neuteboom, M.; Oostenbach, P.; Rehben, E.; Sanden, A. van der
Publisher
International Committee for Animal Recording (ICAR), Roma, Italy
Citation
ICAR Technical Series, 2010, No.14, pp 79-91
AbstractFull Text

Author(s)
Jong, G. de
Publisher
International Committee for Animal Recording (ICAR), Roma, Italy
Citation
ICAR Technical Series, 2010, No.14, pp 323-324
AbstractFull Text

This article discusses the PAINT T (programme evaluation and identification of new Bulls) initiative programme for the genetic improvement of Nelore breeder bulls in CRV Lagoa, Rio de Janeiro, Brazil. PAINT covers two selection indexes (weaning and yearling), and covering visual, performance...

Author(s)
Franzon, C. A.; Carvalheiro, R.; Sesana, J. C.; Sala, V. E.
Publisher
International Committee for Animal Recording (ICAR), Rome, Italy
Citation
ICAR Technical Series, 2017, No.21, pp 47-58
AbstractFull Text

Metabolic stability or resistance to metabolic diseases can be improved by genetic selection. Strategies include direct selection based on clinically observed traits, and indirect selection using indicators or predictors of metabolic diseases. The most prevalent metabolic diseases in dairy cattle,...

Author(s)
Heringstad, B.; Pryce, J. E.; Gaddis, K. L. P.; Koeck, A.; Bastin, C.; Abdelsayed, M.; Gengler, N.; Miglior, F.; Egger-Danner, C.; Stock, K. F.; Bradley, A. J.; Cole, J. B.
Publisher
International Committee for Animal Recording (ICAR), Rome, Italy
Citation
ICAR Technical Series, 2017, No.21, pp 171-176

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